Sensor output change detection

ABSTRACT

A method includes acquiring a first data column output from a plurality of sensors, generating a model for estimating data from the plurality of sensors on the basis of the first data column, acquiring a second data column output from the plurality of sensors, obtaining an estimated data column corresponding to the second data column based on the model by using regularization for making an error between the second data column and the estimated data column sparse, and identifying a sensor in which a change occurred between the first data column and the second data column on the basis of the error between the second data column and the estimated data column. A corresponding computer program product and apparatus are also disclosed herein.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of data processingand more particularly to processing sensor data.

Sensors may be provided on a complicated system such as an automobile ora manufacturing device. Time series data acquired from the sensors maybe analyzed. Particularly, time series data may be analyzed to monitorthe presence or absence of abnormalities even in systems with hundredsof sensors.

SUMMARY

An apparatus includes a first acquisition unit which acquires a firstdata column output from a plurality of sensors, a generation unit whichgenerates a model for estimating data from the plurality of sensors onthe basis of the first data column, a second acquisition unit whichacquires a second data column output from the plurality of sensors, anestimation unit which obtains an estimated data column corresponding tothe second data column based on the model by using regularization formaking an error between the second data column and the estimated datacolumn sparse, and an identification unit which identifies a sensor inwhich a change occurred between the first data column and the seconddata column on the basis of the error between the second data column andthe estimated data column.

A method includes acquiring a first data column output from a pluralityof sensors, generating a model for estimating data from the plurality ofsensors on the basis of the first data column, acquiring a second datacolumn output from the plurality of sensors, obtaining an estimated datacolumn corresponding to the second data column based on the model byusing regularization for making an error between the second data columnand the estimated data column sparse, and identifying a sensor in whicha change occurred between the first data column and the second datacolumn on the basis of the error between the second data column and theestimated data column. A corresponding computer program product andapparatus are also disclosed herein.

The foregoing summary of the invention is not a list of featuresrequired for the present invention. In addition, any sub-combination ofthese features could also constitute the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram illustrating a configuration example of adetection device operably coupled to a plurality of sensors inaccordance with at least one embodiment of the present invention;

FIGS. 1B-1G are equation diagrams in accordance with at least oneembodiment of the present invention;

FIG. 2 is a flowchart illustrating one embodiment of an operation flowin accordance with at least one embodiment of the detection device;

FIG. 3 is a diagram illustrating an example of a simulation result inaccordance with at least one embodiment of the detection device;

FIG. 4 is a diagram illustrating an example of a simulation resultobtained by an existing detection device which calculates the scores ofabnormality degrees of a plurality of sensors;

FIG. 5 is a block diagram illustrating a variation of the detectiondevice; and

FIG. 6 is a block diagram illustrating an example of the hardwareconfiguration of a computer in accordance with at least one embodimentof the detection device.

DETAILED DESCRIPTION

Hereinafter, the present invention will be described in variousembodiments. It should be noted, however, that the following embodimentsare not intended to limit the scope of the appended claims, and that notall the combinations of features described in the embodiments arenecessarily required by the present invention.

The abnormality degrees of sensors may be scored for identification onthe basis of the degree of change in the structure of a relation betweenthe sensors with respect to a sensor group (normal sensors) which showsa detection result in which an input signal is within a normal signalrange and a sensor group (abnormal sensors) which shows a detectionresult in which an input signal is within an abnormal signal range froma plurality of time series data. In the case where, for example, thereare a hundred or more of sensors mutually having a complicatedcorrelation in a physical system, however, it is difficult to pick uponly abnormal sensors and sensors having a strong correlation with anabnormal sensor also have high scores of abnormality in some cases.

FIG. 1A illustrates a configuration example of a detection device 100according to an embodiment along with a plurality of sensors 10. Theplurality of sensors 10 may be mounted on an object such as anautomobile, a ship, an aircraft, or other transport machinery, amanufacturing device, or a monitoring device and transmit detectionresults to the detection device 100. The sensor 10 may be connected tothe detection device 100 by wired communication or alternatively may beconnected to the detection device 100 by wireless communication. Thisembodiment will be described by giving an example in which the object isan automobile.

The sensor 10 may be, for example, a temperature sensor for enginecooling water, a temperature sensor for engine intake air, an oiltemperature sensor, an intake pipe internal pressure sensor for a fuelinjection device, a supercharging pressure sensor for a turbocharger, athrottle position sensor, a steering angle sensor, a vehicle heightsensor, a liquid level sensor, a rotation speed sensor, a knock sensor,an acceleration sensor, an angular velocity sensor, a geomagneticsensor, a flow sensor, an oxygen sensor, a lean air-fuel ratio sensor,or the like. In some cases, several hundred to one thousand or moresensors 10 are provided at a time.

In this case, it is necessary to process several hundred to one thousandor more arrays of time series data. Regarding the time series data ofthe sensors provided on the automobile and the like, data valuesthemselves and the structure of a relation between the sensorsdynamically change and the dynamic change may abruptly occur and may notbe foreseeable in advance. As an example, in the case where a car is“accelerated” by an action of “treading on an accelerator,” thestructure of a relation between the sensors is strengthened with respectto the outputs from the throttle position sensor, the rotation speedsensor, and the acceleration sensor. Specifically, the structure of therelation between the sensors dynamically changes at an unpredictabletime (for example, regarding a user's operation, a situation of theautomobile, or the like) and therefore the sensors may have amutually-complicated correlation.

Moreover, in this case, a detected output at each sensor and a range ofvalues for determining that the detected output is in the normal statevary according to the situation of the automobile such as passengers (aloading amount of baggage or the like), the current speed, the roadgradient during traveling, whether the automobile is traveling on astraight road or on a curved road (in the case of a curved road, thedegree of curvature), or the like. Specifically, an output of eachsensor and criteria for judgment of whether the situation of theautomobile is normal dynamically change at an unpredictable time (forexample, regarding a user's operation, a situation of the automobile, orthe like).

In this manner, even in the case of time-series signals from the samesensor, data values and the criteria for judgment significantly changein response to a change in the structure of a relation with othersensors and therefore it is difficult to perform meaningful processingeven if data is compared with past data. In this case, it is conceivableto treat the sensors as multivariable systems for analysis. Thecalculation amount, however, increases in an exponential manner as thenumber of sensors increases. Therefore, this approach is distant whenusing several hundred to one thousand or more sensors.

Moreover, there can also be an idea of estimating the degree of changein the structure of the relation between the sensors and scoring theabnormality degrees of the sensors according to the estimation result.In this case, it is possible to discriminate a normal sensor from anabnormal sensor by comparing the scored abnormality degree with apredetermined threshold value or the like. When this type ofdiscrimination method is used, however, not only the score of anabnormal sensor, but also the score of a normal sensor having a strongcorrelation with the abnormal sensor is also calculated as a great valueenough to show abnormality in some cases, in the case of scoring theabnormality degrees of the sensors mutually having a complicatedcorrelation.

Accordingly, the detection device 100 according to this embodiment is adetection device which detects a change in outputs from a plurality ofsensors, wherein learning data is used to generate a model for mappingoutput data from the plurality of sensors 10 to a low-dimensional latentspace and then reconstructing the output data in the originaldimensions. Furthermore, in the case of using the model to map test datato a latent space and to reconstruct the test data in the originaldimensions, the detection device 100 performs regularization so as toenlarge a change of data which behaves abnormally, thereby increasingthe score of the abnormal sensor in comparison with the normal sensors.The detection device 100 includes a first acquisition unit 110, a secondacquisition unit 120, a storage unit 130, a generation unit 140, anestimation unit 150, and an identification unit 160.

The first acquisition unit 110 acquires a first data column output fromthe plurality of sensors 10. The first acquisition unit 110 acquires thefirst data column as learning data. Moreover, the first acquisition unit110 may acquire a first data column stored in the detection device 100or an external storage device. Furthermore, the first acquisition unit110 may acquire a first data column supplied by an external deviceconnected to the plurality of sensors 10.

The first acquisition unit 110 preferably acquires outputs from theplurality of sensors 10 in a state where the outputs represent a normalbehavior of the automobile which is a measuring object as a first datacolumn for learning. Alternatively, the first acquisition unit 110 mayacquire a supposed data column as the first data column from aprediction model for generating a data column supposed to be output fromthe plurality of sensors 10, a model for a device to be measured, or thelike.

This embodiment will be described by giving an example that the firstacquisition unit 110 acquires a first data column corresponding to firstoutputs output in time series by the plurality of sensors 10 when theautomobile, which is an object equipped with the plurality of sensors10, is in a normal state. The first acquisition unit 110 supplies theacquired first data column to the storage unit 130.

The second acquisition unit 120 acquires a second data column outputfrom the plurality of sensors 10. The second acquisition unit 120acquires the second data column as test data. Moreover, the secondacquisition unit 120 may acquire a second data column stored in thedetection device 100 or an external storage device. Furthermore, thesecond acquisition unit 120 may acquire a second data column supplied byan external device connected to the plurality of sensors 10.

The second acquisition unit 120 acquires a second data column detectedfrom the automobile which is a measuring object. For example, the secondacquisition unit 120 acquires outputs in a predetermined period, whichis different from the period in which the first acquisition unit 110acquires the first data column, as a second data column.

In this case, the second acquisition unit 120 preferably acquires data,which is output in time series from the plurality of sensors 10, as asecond data column in the case where the automobile is operating. Thisembodiment will be described by giving an example that the secondacquisition unit 120 acquires a second data column corresponding tosecond outputs output in time series from the plurality of sensors 10 inthe case where the automobile equipped with the plurality of sensors 10is in the operating state. The second acquisition unit 120 may supplythe acquired second data column to the storage unit 130.

The storage unit 130, which is connected to the first acquisition unit110 and to the second acquisition unit 120, stores the first data columnand the second data column which have been received. Moreover, thestorage unit 130 may store data generated by the detection device 100and intermediate data processed in the course of generating the data orthe like. Furthermore, the storage unit 130 may supply stored data to arequestor in response to a request from each unit in the detectiondevice 100.

The generation unit 140, which is connected to the storage unit 130,generates a model for estimating data from the plurality of sensors 10on the basis of the first data column. The generation unit 140 generatesa model known as a latent variable model, as a probability model whichrepresents the operation of the plurality of sensors 10. The generationunit 140 calculates a first latent data column, which is a latentvariable data column, from the first data column on the basis of thelatent variable model. In the above, the latent variable is not adirectly-observed variable (physical quantity), but a variableindirectly estimated through a variation pattern of various data. Thelatent variable is used for purpose of representing a state or the likebehind a sampled physical quantity and is a variable known in theprobability model.

For each data output from the plurality of sensors 10, the generationunit 140 generates a model which represents a probability distributionof latent data corresponding to the data concerned and of estimated dataobtained from the latent data. The generation unit 140 generates aprobability model for generating the estimated data obtained byestimating outputs from the plurality of sensors 10 after reconstructionfrom the latent data. The generation unit 140 may supply the generatedprobability model to the storage unit 130.

The estimation unit 150 estimates an estimated data column correspondingto the second data column based on the model generated by the generationunit 140 by using regularization in which an error between the seconddata column and the estimated data column is made sparse. Note here thatthe term “sparse” means a matrix state in which nonzero components arevery few, in other words, in which most components are zero. Therefore,the estimation unit 150 adds a regularization term to a model generatedby the generation unit 140 so that most of the components of adifference between the second data column and the estimated data columnare zero.

The identification unit 160 identifies a sensor where a change occurredbetween the first data column and the second data column on the basis ofan error between the second data column and the estimated data column.The identification unit 160 identifies, for example, the sensor 10corresponding to a nonzero component in a difference between the seconddata column and the estimated data column as a sensor where a changeoccurred.

Moreover, the identification unit 160 may identify a sensor in whichabnormality is detected on the basis of an error between the second datacolumn and the estimated data column. The identification unit 160 mayidentify the sensor 10 corresponding to the nonzero component as anabnormal sensor in the difference between the second data column and theestimated data column. Alternatively, the identification unit 160 mayidentify the sensor 10 corresponding to a component having a value equalto or greater than a predetermined value among the nonzero components asan abnormal sensor.

The detection device 100 according to this embodiment identifies anabnormal sensor by generating a probability model based on the firstdata column output from the plurality of sensors 10 and obtainingestimated data by adding a regularization term in which an error betweenthe second data column and the estimated data based on the second datacolumn is made sparse to the model. The operation of the detectiondevice 100 will be described by using FIG. 2.

FIG. 2 illustrates an operation flow of the detection device 100according to this embodiment. The detection device 100 performs theoperation flow to identify a small number of abnormal sensors among theplurality of sensors 10 accurately.

First, the first acquisition unit 110 acquires a first data column(S210). For example, when the number of the plurality of sensors 10 isD, the first acquisition unit 110 acquires data of N×D columns per rowas a first data column y_(n) (n=1, 2, . . . , N), where y_(n) is avector having D elements and a data column represented in D dimensions.The first acquisition unit 110 may receive the vector of the first datacolumn in an array format. Moreover, the storage unit 130 may store thevector of the first data column in an array format.

Subsequently, the second acquisition unit 120 acquires a second datacolumn (S220). The second acquisition unit 120 acquires, for example,data of M×N columns per row as a second data column η_(m) (m=1, 2, . . ., M), where η_(m) is a vector having D elements and a data columnrepresented in D dimensions.

Subsequently, the generation unit 140 calculates a first latent datacolumn which is a latent variable data column mapped from the first datacolumn y_(n) to the latent space on the basis of a latent variable model(S230), where the generation unit 140 is allowed to use a known modelsuch as, for example, the graphical Gaussian model (Graphical LASSO),probabilistic PCA (Principal Component Analysis), probabilistic kernelPCA, or Gaussian process latent variable model, as the latent variablemodel.

The following describes an example that the generation unit 140 of thisembodiment uses a known model as a Laplacian eigenmap latent variablemodel. The generation unit 140 calculates a first latent data columnx_(n) by minimizing the expression of FIG. 1B on the basis of thedepicted model.

In the expression of FIG. 1B, “argmin f(x)” indicates x in the casewhere f(x) is minimum, “tr” indicates the sum of diagonal elements(trace), and “subject to g(z)” indicates the meaning of “under theconstraint condition g(z).” Moreover, a vector I indicates a unit matrix(a_(ij)=1 (i=j), a_(ij)=0 (i≠j)) and a vector 1 indicates a row vectorin which all elements are 1.

Moreover, the generation unit 140 calculates the first latent datacolumn x_(n) as a predetermined Q-dimensional data column lower indimensions than the D dimensions. For example, the generation unit 140calculates the first latent data column x_(n) in low dimensions such asthree or four dimensions and shows the behaviors of the plurality ofsensors 10 in the corresponding three- or four-dimensional latent space.In FIG. 1B, the matrices L and D are represented by the expressionsshown in FIG. 1C.

In the expressions shown in FIG. 1C, w_(n,m) is an element of W and“diag(x)” indicates a diagonal matrix. Moreover, σ is a predeterminedparameter. For example, when σ increases, the value of w_(n,m), which isan element of the matrix W, approaches zero unless a difference betweeny_(n) and y_(m) increase in response to σ. Moreover, when σ decreases,the value of w_(n,m), which is an element of the matrix W, increasesunless the difference between y_(n) and y_(m) decrease in response to σ.Specifically, σ serves as a parameter which determines to what extentthe difference between y_(n) and y_(m) is reflected on the matrix W.

Subsequently, the generation unit 140 generates a model representing aprobability distribution of the second latent data column χ_(m) andestimated data obtained from the second latent data on the basis of thefirst data column y_(n), the first latent data column x_(n), and thesecond data column η_(m) (S240). The generation unit 140 generates ajoint probability density function p(χ_(m),v_(m)|η_(m),y_(n)) of χ_(m)and v_(m) as represented by the expression shown in FIG. 1D, where v_(m)is the estimated data obtained from the second latent data column χ_(m).

In the expressions shown in FIG. 1D, σx and σy are predeterminedparameters similarly to σ in FIG. 1C. The expressions of FIG. 1D arerepresented by terms of the norm of x_(n) and χ_(m), the norm of y_(n)and vm, and the norm of y_(n) and η_(m), except constants and parametersσx and σy. Specifically, the generation unit 140 generates a modelrepresenting a probability distribution where the probability decreasesas the difference between the latent data in the second latent datacolumn χ_(m), and each latent data of the first latent data column x_(n)increases, the probability decreases as the difference between theestimated data in the estimated data column v_(m) and each data of thefirst data column y_(n) increases, and the probability decreases as thedifference between data in the second data column η_(m) and each data ofthe first data column y_(n) increases. In this manner, the generationunit 140 generates a model in which the joint probability densityfunction p of χ_(m) and v_(m) increases as the values of respective dataof x_(n) and χ_(m), y_(n) and v_(m), and y_(n) and η_(m) approach eachother.

The estimation unit 150 estimates an estimated data column byreconstruction with sparse regularization on the basis of test datawhich is an observed value in the known latent variable model asdescribed above (S250). For example, the estimation unit 150 calculatesthe estimated data column v_(m) by using regularization in which anerror between the second data column η_(m) and the estimated data columnv_(m) is concentrated on a data column obtained from particular sensors10.

Specifically, the estimation unit 150 calculates the estimated datacolumn v_(m) by using a regularization term in which the probabilitydecreases as the difference between data in the second data column η_(m)and estimated data in the estimated data column v_(m) increases. Theestimation unit 150 optimizes the joint probability density function pby using the regularization term as described above and calculates theestimated data column v_(m) corresponding to the second latent datacolumn χ_(m), thereby reconstructing a data column substantiallycoincident with the second data column η_(m).

Note here that the second data column η_(m) is a data column acquiredwhen most sensors 10 normally operate and therefore the estimated datacolumn v_(m) obtained by the estimation unit 150 substantially coincideswith an output data column indicating the behavior of the normal sensorin the N-dimensional superspace. Therefore, the estimation unit 150calculates the estimated data column v_(m) so that the datacorresponding to an abnormal sensor indicates the behavior differentfrom the behavior of data corresponding to the normal sensor among thedata estimated from the second data column η_(m) (in other words, sothat the error is large).

Moreover, the estimation unit 150 may calculate the estimated datacolumn by using regularization in which the error between the seconddata column η_(m) and the estimated data column v_(m) is concentrated inthe time direction with respect to the respective sensors. For example,the estimation unit 150 calculates the estimated data column by using aregularization term in which the probability decreases as the differencebetween the estimated data in the estimated data column v_(m) and theestimated data in the estimated data column v_(m-1) which has beenestimated before increases. The estimation unit 150 calculates theestimated data column v_(m) by using the regularization term asdescribed above, thereby reconstructing a data column substantiallycoincident with the estimated data column v_(m-1) which has beenestimated before.

Note here that the second data column η_(m) is a data column acquiredwhen most sensors 10 operate normally and therefore the estimated datacolumn v_(m) obtained by the estimation unit 150 substantially coincideswith output data indicating the behavior of the sensor 10 which operatesnormally in a temporally stable manner among data of the estimated datacolumn v_(m-1) which has been estimated before. Therefore, theestimation unit 150 calculates the estimated data column v_(m) so thatthe data corresponding to an abnormal sensor indicates the behaviordifferent from the behavior of data corresponding to the normal sensorin comparison with the estimated data column v_(m-1) which has beenestimated before (in other words, so that the error is large).

Moreover, the estimation unit 150 may calculate the estimated datacolumn by using regularization in which the error between the seconddata column η_(m) and the estimated data column v_(m) is concentrated ona data column obtained from particular sensors 10 and the error betweenthe second data column η_(m) and the estimated data column v_(m) isconcentrated in the time direction with respect to the respectivesensors. Specifically, the estimation unit 150 calculates the estimateddata column v_(m) by using a regularization term in which theprobability decreases as the difference between the data in the seconddata column η_(m) and the estimated data in the estimated data columnv_(m) increases and the probability decreases as the difference betweenthe estimated data in the estimated data column v_(m) and the estimateddata in the estimated data column v_(m-1) which has been estimatedbefore increases.

As described above, the estimation unit 150 calculates the estimateddata column v_(m) by using the term for regularization. Morespecifically, as represented by the expression of FIG. 1E, theestimation unit 150 estimates the second latent data column and theestimated data column which optimize an objective function including aterm in which the probability of taking the value of latent data in thesecond latent data column χ_(m) which is a latent variable data columnof the second data column η_(m) and the value of the correspondingestimated data in the estimated data column v_(m) is summed up withrespect to the second data column η_(m) and a term for regularization.

The first term of the right side of FIG. 1E is a term in which the jointprobability density function p(χ_(m), v_(m)|η_(m), y_(n)) of χ_(m) andv_(m) illustrated in FIG. 1D is summed up with respect to the seconddata column η_(m) which is test data. Moreover, the second term is aterm for regularization. The estimation unit 150 uses, for example, aregularization term shown in the expression of FIG. 1F as a term forregularization.

The first term in the right side of FIG. 1F is a regularization term inwhich the probability decreases as the difference between the data inthe second data column η_(m) and the estimated data in the estimateddata column v_(m) increases, and the first term is calculated on thebasis of the sum of squared values in the respective dimensions.Moreover, the second term is a regularization term in which theprobability decreases as the difference between the estimated data inthe estimated data column v_(m) and the estimated data in the estimateddata column v_(m-1) which has been estimated before increases, and thesecond term is calculated on the basis of the sum (L1 norm) of theabsolute values of the values in the respective dimensions.

In the above, λ1 and λ2 are parameters for determining the weight of theregularization term to be used. For example, when λ1=1 and λ2=0 areassumed, the estimation unit 150 thereby uses the regularization of thefirst term of FIG. 1F. Moreover, when λ1=0 and λ2=1 are assumed, theestimation unit 150 thereby uses regularization of the second term.Furthermore, when λ1≠0 and λ1≠0 are assumed, the estimation unit 150thereby uses regularization of the first and second terms. λ1 and λ2 maybe previously determined according to a purpose or alternatively may beadjusted according to an actual detection result of the detection device100.

As described above, the estimation unit 150 is able to calculate thesecond latent data column χ_(m)′ and the estimated data column v_(m)′ byoptimizing the objective function of FIG. 1E (in other words, byacquiring χ_(m) and v_(m) which minimize the objective function) byusing the regularization terms of FIG. 1F. Note here that the symbol “′”in the left side of FIG. 1E indicates a value determined byoptimization.

Subsequently, the identification unit 160 identifies a sensor where achange occurred between the first data column y_(n) and the second datacolumn η_(m) (S260). The identification unit 160 identifies, forexample, the sensor 10 corresponding to a component in which the scorebased on the difference between the second data column η_(m) and theestimated data column v_(m)′ represented by the expression of FIG. 1G isnonzero, as a sensor where a change occurred.

Instead of the score function of FIG. 1G, the identification unit 160may use a difference between the second data column η_(m) and theestimated data column v_(m)′ in each dimension d (the maximum value isD) as a score value or may use an expression in which the sum total iscalculated with respect to the dimension d in the right side of FIG. 1Gas a score function. The identification unit 160 may identify a sensor10 where the score is nonzero or equal to or greater than apredetermined value as an abnormal sensor. The estimation unit 150calculates the estimated data column v_(m)′ in which an error betweendata corresponding to a normal sensor and data corresponding to anabnormal sensor is made larger on the basis of the second data columnη_(m). Therefore, the identification unit 160 is able to identify onlythe abnormal sensor.

Moreover, even if the number of sensors 10 increases to exceed onehundred, for example, the detection device 100 is able to easily performprocessing by increasing the dimension number D of the first data columny_(n) and the second data column η_(m) correspondingly. Therefore, thedetection device 100 is able to determine only the sensors 10 falling inan abnormal signal range even if there are, for example, 100 or moresensors mutually having a complicated correlation in a physical system.

FIG. 3 illustrates an example of a simulation result obtained by thedetection device 100 according to this embodiment which calculates thescores of abnormality degrees of the plurality of sensors 10. Moreover,FIG. 4 illustrates an example of a simulation result obtained by anexisting detection device which calculates the scores of abnormalitydegrees of the sensors. FIGS. 3 and 4 each illustrate a simulationresult with respect to outputs of the plurality of sensors which detectthe positions of 30 mass points with respect to a physical model withthe 30 mass points connected by springs. Since the plurality of masspoints are connected by the plurality of springs, the positions of therespective mass points vary in a complicated manner and thecorresponding sensors output detection results mutually having acomplicated correlation.

The horizontal axis of FIGS. 3 and 4 represents each sensor whichdetects the corresponding mass point and the vertical axis representsthe score value calculated so as to correspond to the sensor. Moreover,FIGS. 3 and 4 each illustrate a result of simulation in which thedetection device scores the plurality of sensors in a state where onlymass point 3 deviates from the normal behavior.

FIG. 3 illustrates a result of calculation by the detection device 100according to this embodiment with respect to the scores of abnormalitydegrees of the plurality of sensors 10, from which it is understood thatonly the sensor detecting the mass point 3 outputs a score remarkablyhigh in comparison with other sensors. Meanwhile, FIG. 4 illustrates aresult of calculation by the existing detection device with respect tothe scores of abnormality degrees of the sensors, which shows a resultthat, while the score value of the mass point 3 is high, the scorevalues of other mass points having a strong correlation with the masspoint 3 are also high.

For example, in the case of generating a prediction model from learningdata, the existing detection device learns the model by using aregularization term and detects an abnormal sensor corresponding to testdata by using the learned model. Therefore, if one sensor outputsabnormality and sensors having a strong correlation with this sensoralso output detection results different from those of the ordinary caseand then test data including those detection results are used due to theeffect of the sensor concerned, the existing detection device sometimesoutputs a high score value for not only the sensor concerned, but alsofor the sensors having the strong correlation with the sensor, as aresult outside the expectation based on the learning data, asillustrated in FIG. 4.

Meanwhile, the detection device 100 according to this embodimentcalculates an estimated data column by using a regularization term in astage of reconstruction with the test data, instead of the learningstage. Therefore, in the case where one sensor outputs abnormality, thedetection device 100 is able to detect that the outputs from othersensors are within the normal operations based on the test data and isable to output the score values of the sensors so that only the scorevalue of the sensor concerned is high as illustrated in FIG. 3, even ifother sensors having a strong correlation with the sensor concernedoutput detection results different from those of the ordinary case dueto the effect of the sensor concerned.

FIG. 5 illustrates a variation of the detection device 100 according tothis embodiment along with the plurality of sensors 10. The samereference numerals are used for the units performing substantially thesame operations as those of the detection device 100 according to theembodiment illustrated in FIG. 1A in the detection device 100 of thevariation and the description thereof is omitted here. The detectiondevice 100 according to the variation further includes an adjustmentunit 210.

The adjustment unit 210 is connected to the storage unit 130, theestimation unit 150, and the identification unit 160 and adjusts theweights λ1 and λ2 of the regularization used by the estimation unit 150so that a sensor 10 identified by the identification unit 160 coincideswith a known sensor 10 in which a change occurred. In this case, thesecond acquisition unit 120 acquires a second data column η_(m) in whicha sensor where a change occurred is known for the first data columny_(n). Furthermore, the detection device 100 identifies an abnormalsensor by performing the operation flow of FIG. 2 on the basis of thefirst data column y_(n) and the second data column η_(m) and thereafterthe adjustment unit 210 adjusts the values of λ1 and λ2 so that theidentification result of the identification unit 160 corresponds to thechange information of the known sensor.

The adjustment unit 210 may update the values of λ1 and λ2 to performloop processing of repeating the adjustment of the values of λ1 and λ2 aplurality of times. In this manner, the detection device 100 accordingto the variation is able to adjust the weights of the regularization onthe basis of an actual identification result and therefore is able toscore the abnormality degrees of the sensors more accurately.

In the variation of the detection device 100 according to theembodiment, it has been described that the adjustment unit 210 adjuststhe values of the weights λ1 and λ2 of the regularization. Alternativelyor in addition to this, the adjustment unit 210 may adjust the values ofthe parameters σ, σx, and σy. This enables the detection device 100 toincrease the accuracy of the scores of the sensors 10 by easilyperforming the parameter adjustment.

FIG. 6 illustrates an example of a hardware configuration of a computer1900 which functions as the detection device 100 according to theembodiment. The computer 1900 according to the embodiment includes a CPUperipheral unit, an input/output unit, and a legacy input/output unit.The CPU peripheral unit includes a CPU 2000, a RAM 2020, and a graphicscontroller 2075, all of which are mutually connected to one another viaa host controller 2082. The CPU peripheral unit also includes a displaydevice 2080. The input/output unit includes a communication interface2030, a hard disk drive 2040, and a DVD drive 2060, all of which areconnected to the host controller 2082 via an input/output controller2084. The legacy input/output unit includes a ROM 2010, a flexible diskdrive 2050, and an input/output chip 2070, all of which are connected tothe input/output controller 2084.

The host controller 2082 mutually connects the RAM 2020 to the CPU 2000and the graphics controller 2075, both of which access the RAM 2020 at ahigh transfer rate. The CPU 2000 operates on the basis of a programstored in the ROM 2010 and the RAM 2020, and controls each of thecomponents. The graphics controller 2075 acquires image data generatedby the CPU 2000 or the like in a frame buffer provided in the RAM 2020,and causes the display device 2080 to display the acquired image data.In place of this, the graphics controller 2075 may internally include aframe buffer in which the image data generated by the CPU 2000 or thelike is stored.

The input/output controller 2084 connects the host controller 2082 tothe communication interface 2030, the hard disk drive 2040, and the DVDdrive 2060, all of which are relatively high-speed input/output devices.The communication interface 2030 communicates with another device via anetwork. The hard disk drive 2040 stores, therein, a program and data tobe used by the CPU 2000 in the computer 1900. The DVD drive 2060 reads aprogram or data from a DVD-ROM 2095 and provides the read program ordata to the hard disk drive 2040 via the RAM 2020.

In addition, the input/output controller 2084 is connected to relativelylow-speed input/output devices such as the ROM 2010, the flexible diskdrive 2050, and the input/output chip 2070. The ROM 2010 stores aprogram such as a boot program executed at a start-up time of thecomputer 1900 and/or a program depending on hardware of the computer1900 or the like. The flexible disk drive 2050 reads a program or datafrom a flexible disk 2090, and provides the read program or data to thehard disk drive 2040 via the RAM 2020. The input/output chip 2070connects the flexible disk drive 2050 to the input/output controller2084 and also connects various kinds of input/output devices to theinput/output controller 2084 through a parallel port, a serial port, akeyboard port, a mouse port, and the like, for example.

A program to be provided to the hard disk drive 2040 via the RAM 2020 isprovided by a user with the program stored in a recording medium such asthe flexible disk 2090, the DVD-ROM 2095, or an IC card. The program isread from the storage medium, then installed in the hard disk drive 2040in the computer 1900 via the RAM 2020, and executed by the CPU 2000.

The program is installed in the computer 1900 to cause the computer 1900to function as the first acquisition unit 110, the second acquisitionunit 120, the storage unit 130, the generation unit 140, the estimationunit 150, the identification unit 160, and the adjustment unit 210.

Information processes written in the programs are read by the computer1900 and thereby functions as the first acquisition unit 110, the secondacquisition unit 120, the storage unit 130, the generation unit 140, theestimation unit 150, the identification unit 160, and the adjustmentunit 210, all of which are specific means resulting from cooperation ofsoftware and the aforementioned various types of hardware resources.Then, the detection device 100 specific to an intended purpose is builtup by performing computation or processing of information in accordancewith the intended purpose of the computer 1900 in this embodiment by useof such specific means.

In a case where communications between the computer 1900 and an externaldevice or the like are performed, for example, the CPU 2000 executes acommunication program loaded on the RAM 2020 and instructs thecommunication interface 2030 on the basis of processing contentsdescribed in the communication program to perform communicationprocessing. Upon receipt of the control from the CPU 2000, thecommunication interface 2030 reads transmission data stored in atransmission buffer region or the like provided in a storage device suchas the RAM 2020, the hard disk drive 2040, the flexible disk 2090, orthe DVD-ROM 2095 and then transmits the data to a network or writesreception data received from the network into a receiving buffer regionor the like provided on a storage device. As described above, thecommunication interface 2030 is allowed to transfer transmission andreception data between itself and a storage device by a direct memoryaccess (DMA) scheme. Instead of this, the CPU 2000 is also allowed toread data from a storage device or a communication interface 2030 of atransfer source and then to transfer transmission and reception data bywriting the data into a communication interface 2030 or a storage deviceof a transfer destination.

In addition, the CPU 2000 causes all or a required portion of data to beread from a file or a database stored in an external storage device suchas the hard disk drive 2040, the DVD drive 2060 (DVD-ROM 2095), theflexible disk drive 2050 (flexible disk 2090) or the like into the RAM2020 by DMA transfer or the like, and then performs various kinds ofprocessing for the data in the RAM 2020. Then, the CPU 2000 writes theprocessed data back in the external storage device by DMA transfer orthe like. In such processing, since the RAM 2020 can be considered as adevice in which the contents of an external storage device are storedtemporarily, the RAM 2020 and an external storage device or the like arecollectively termed as a memory, a storage unit, a storage device, orthe like in this embodiment. Various types of information includingvarious types of programs, data, tables, databases, and the like in thisembodiment are stored in such a storage device and are handled as aninformation processing target. It should be noted that the CPU 2000 isallowed to retain a part of data in the RAM 2020 in a cache memory andthen to read and write the data in the cache memory. In this case aswell, since the cache memory partially shares the function of the RAM2020, the cache memory is considered to be included in the RAM 2020, amemory and/or a storage device except for a case where the cache memoryneeds to be distinguished from the RAM 2020, the memory, and/or thestorage device in this embodiment.

In addition, the CPU 2000 performs, on the data read from the RAM 2020,various types of processing being specified by a sequence ofinstructions of the program and including various types of computations,information processing, conditional judgment, information retrieval andreplacement and the like described in this embodiment, and writes theprocessed data back in the RAM 2020. In a case where the CPU 2000performs conditional judgment, for example, the CPU 2000 determines bycomparing a variable with the other variable or constant whether or noteach of various types of variables indicated in the present embodimentsatisfies a condition that the variable is larger, smaller, not less,not greater, equal, or the like. In the case where the condition issatisfied (or the condition is not satisfied), the processing of the CPU2000 branches to a different instruction sequence or calls a subroutine.

In addition, the CPU 2000 is capable of searching for information storedin a file, a database, or the like in a storage device. For example, ina case where multiple entries having attribute values of a firstattribute respectively associated with attribute values of a secondattribute are stored in a storage device, the CPU 2000 searches themultiple entries stored in the storage device for an entry whoseattribute value of the first attribute matches a specified condition.Then, the CPU 2000 reads an attribute value of the second attributestored in the entry, and thereby, obtains the attribute value of thesecond attribute that satisfies the predetermined condition and that isassociated with the first attribute.

The programs or modules described above may be stored as a computerprogram product on a computer readable storage medium such as anexternal recording medium. As the recording medium, any one of thefollowing media may be used: an optical recording medium such as a DVD,Blu-ray®, or a CD; a magneto-optic recording medium such as an MO; atape medium; and a semiconductor memory such as an IC card, in additionto the flexible disk 2090 and the DVD-ROM 2095. Alternatively, theprogram may be provided to the computer 1900 via a network, by using, asa recording medium, a storage device such as a hard disk or a RAMprovided in a server system connected to a private communication networkor the Internet.

The present invention has been described hereinabove with reference topreferred embodiments. The technical scope of the present invention,however, is not limited to the above-described embodiments only. It isapparent to one skilled in the art that various modifications orimprovements may be made to the above-described embodiments.Accordingly, it is also apparent from the scope of the claims that theembodiments with such modifications or improvements added thereto can beincluded in the technical scope of the invention.

It should be noted that the operations, procedures, steps, and stages ofeach process performed by an apparatus, system, program, and methodshown in the claims, embodiments, or diagrams can be performed in anyorder as long as the order is not indicated by “prior to,” “before,” orthe like and as long as the output from a previous process is not usedin a later process. Even if the operation flow is described usingphrases such as “first” or “next” in the claims, embodiments, ordiagrams, it does not necessarily mean that the process must beperformed in this order.

It should be noted that the apparatuses disclosed herein may beintegrated with additional circuitry within integrated circuit chips.The resulting integrated circuit chips can be distributed by thefabricator in raw wafer form (that is, as a single wafer that hasmultiple unpackaged chips), as a bare die, or in a packaged form. In thelatter case, the chip is mounted in a single chip package (such as aplastic carrier, with leads that are affixed to a motherboard or otherhigher level carrier) or in a multichip package (such as a ceramiccarrier that has either or both surface interconnections or buriedinterconnections). In any case, the chip is then integrated with otherchips, discrete circuit elements, and/or other signal processing devicesas part of either (a) an intermediate product, such as a motherboard, or(b) an end product. The end product can be any product that includesintegrated circuit chips, ranging from toys and other low-endapplications to advanced computer products having a display, a keyboardor other input device, and a central processor.

It should be noted that this description is not intended to limit theinvention. On the contrary, the embodiments presented are intended tocover some of the alternatives, modifications, and equivalents, whichare included in the spirit and scope of the invention as defined by theappended claims. Further, in the detailed description of the disclosedembodiments, numerous specific details are set forth in order to providea comprehensive understanding of the claimed invention. However, oneskilled in the art would understand that various embodiments may bepracticed without such specific details.

Although the features and elements of the embodiments disclosed hereinare described in particular combinations, each feature or element can beused alone without the other features and elements of the embodiments orin various combinations with or without other features and elementsdisclosed herein.

This written description uses examples of the subject matter disclosedto enable any person skilled in the art to practice the same, includingmaking and using any devices or systems and performing any incorporatedmethods. The patentable scope of the subject matter is defined by theclaims, and may include other examples that occur to those skilled inthe art. Such other examples are intended to be within the scope of theclaims.

What is claimed is:
 1. A method for identifying abnormal output by asensor comprising: measuring a first output for the sensor; acquiring afirst data column based on the first output; generating a Laplacianeigenmap latent variable model for estimating data from the sensor basedon the first data column; measuring a second output for the sensor;acquiring a second data column based on the second output; obtaining anestimated data column corresponding to the second data column based onthe Laplacian eigenmap latent variable model; identifying that a changeoccurred between the first data column and the second data column basedon a non-zero difference between the second data column and theestimated data column; and outputting the non-zero difference and anidentification that the sensor generated an abnormal output.
 2. Themethod of claim 1, wherein the estimated data column is calculated byusing regularization in which an error between the second data columnand the estimated data column is concentrated on a data column from thesensor.
 3. The method of claim 2, wherein the error between the seconddata column and the estimated data column is concentrated in a timedirection with respect to the sensor.
 4. The method of claim 1, furthercomprising: calculating a first latent data column which is a latentvariable data column from the first data column and generating theLaplacian eigenmap latent variable model representing a probabilitydistribution of latent data corresponding to data output from the sensorand of estimated data obtained from the latent data for the data.
 5. Themethod of claim 4, further comprising: estimating a second latent datacolumn and the estimated data column which optimize an objectivefunction including a term in which the probability of taking the valueof latent data in the second latent data column which is a latentvariable data column of the second data column and the value of thecorresponding estimated data in the estimated data column is summed upwith respect to the second data column and a term for regularization. 6.The method of claim 5, further comprising: generating the Laplacianeigenmap latent variable model representing a probability distributionwhere the probability decreases as a difference between the latent datain the second latent data column and each latent data of the firstlatent data column increases, wherein the probability decreases as adifference between the estimated data in the estimated data column andeach data of the first data column increases, and wherein theprobability decreases as a difference between the data in the seconddata column and each data of the first data column increases.
 7. Themethod of claim 1, further comprising: acquiring the second data columnin which a sensor where a change occurred is known for the first datacolumn; and adjusting weights of regularization so that an identifiedsensor coincides with the sensor that generated the abnormal output. 8.The method of claim 1, further comprising: acquiring the first datacolumn for learning indicating a normal behavior of a measuring object;acquiring the second data column detected from the measuring object; andidentifying a sensor in which abnormality is detected based on an errorbetween the second data column and the estimated data column.
 9. Acomputer program product for identifying abnormal output by a sensorcomprising one or more non-transitory computer readable storage mediaand program instructions stored on the one or more non-transitorycomputer readable storage media, the program instructions comprisinginstructions to perform a method comprising: measuring a first outputfor the sensor; acquiring a first data column based on the first output;generating a Laplacian eigenmap latent variable model for estimatingdata from the sensor based on the first data column; measuring a secondoutput for the sensor; acquiring a second data column based on thesecond output; obtaining an estimated data column corresponding to thesecond data column based on the Laplacian eigenmap latent variablemodel; identifying that a change occurred between the first data columnand the second data column based on a non-zero difference between thesecond data column and the estimated data column; and outputting thenon-zero difference and an identification that the sensor generated anabnormal output.
 10. The computer program product of claim 9, whereinthe estimated data column is calculated by using regularization in whichan error between the second data column and the estimated data column isconcentrated on a data column from the sensor.
 11. The computer programproduct of claim 10, wherein the error between the second data columnand the estimated data column is concentrated in a time direction withrespect to the sensor.
 12. The computer program product of claim 9, themethod further comprising: calculating a first latent data column whichis a latent variable data column from the first data column andgenerating the Laplacian eigenmap latent variable model representing aprobability distribution of latent data corresponding to data outputfrom the sensor and of estimated data obtained from the latent data forthe data.
 13. The computer program product of claim 12, the methodfurther comprising: estimating a second latent data column and theestimated data column which optimize an objective function including aterm in which the probability of taking the value of latent data in thesecond latent data column which is a latent variable data column of thesecond data column and the value of the corresponding estimated data inthe estimated data column is summed up with respect to the second datacolumn and a term for regularization.
 14. The computer program productof claim 13, the method further comprising: generating the Laplacianeigenmap latent variable model representing a probability distributionwhere the probability decreases as a difference between the latent datain the second latent data column and each latent data of the firstlatent data column increases, wherein the probability decreases as adifference between the estimated data in the estimated data column andeach data of the first data column increases, and wherein theprobability decreases as a difference between the data in the seconddata column and each data of the first data column increases.
 15. Thecomputer program product of claim 9, the method further comprising:acquiring the second data column in which a sensor where a changeoccurred is known for the first data column; and adjusting weights ofregularization so that an identified sensor coincides with the sensorthat generated the abnormal output.
 16. The computer program product ofclaim 9, the method further comprising: acquiring the first data columnfor learning indicating a normal behavior of a measuring object;acquiring the second data column detected from the measuring object; andidentifying a sensor in which abnormality is detected based on an errorbetween the second data column and the estimated data column.
 17. Asystem for identifying abnormal output by a sensor comprising: a memoryand a processor configured to perform a method comprising: measuring afirst output for the sensor; acquiring a first data column based on thefirst output; generating a Laplacian eigenmap latent variable model forestimating data from the sensor based on the first data column;measuring a second output for the sensor; acquiring a second data columnbased on the second output; obtaining an estimated data columncorresponding to the second data column based on the Laplacian eigenmaplatent variable model; identifying that a change occurred between thefirst data column and the second data column based on a non-zerodifference between the second data column and the estimated data column;and outputting the non-zero difference and an identification that thesensor generated an abnormal output.
 18. The system of claim 17, whereinthe estimated data column is calculated by using regularization in whichan error between the second data column and the estimated data column isconcentrated on a data column from the sensor.
 19. The system of claim18, wherein the error between the second data column and the estimateddata column is concentrated in a time direction with respect to thesensor.
 20. The system of claim 17, the method further comprising:calculating a first latent data column which is a latent variable datacolumn from the first data column and generating the Laplacian eigenmaplatent variable model representing a probability distribution of latentdata corresponding to data output from the sensor and of estimated dataobtained from the latent data for the data; and estimating a secondlatent data column and the estimated data column which optimize anobjective function including a term in which the probability of takingthe value of latent data in the second latent data column which is alatent variable data column of the second data column and the value ofthe corresponding estimated data in the estimated data column is summedup with respect to the second data column and a term for regularization.